According to one report, business executives mentioned Artificial Intelligence (AI) more than 30,000 times in earnings calls at the end of 2023. AI, and debates around fears, capabilities and ethics have dominated discussions in both the board room and at the water cooler in most industries. I’ve experienced several major technology shifts and innovations throughout my career but the buzz around AI is groundbreaking.
In behavioral health, we’re talking about AI every day and uncovering how it can be a great complement to other technologies used in treatment centers and practices. Our provider clients have reported it boosts note-taking and documentation processes with improvements in accuracy and efficiency. Data produced by Eleos shows providers have reported that documentation time has been reduced up to 50%, allowing clinical teams to spend more focused time with patients.
Providers leveraging AI also said they have 90% of their notes submitted within 24 hours, reducing documentation backlog and avoiding denials due to late submissions. Another key benefit is care teams indicate they’re able to use AI insights to deliver evidence-based best practices, which is excellent for improving patient outcomes.
Testing the waters
Our colleagues at All Points North (APN), a multi-site, 77-bed Behavioral Health system based in Colorado, decided to move ahead with an AI solution. APN was already using Kipu’s EMR, so they chose to go with Eleos, which is integrated with the Kipu EMR. Eleos focuses on supporting documentation and note taking in therapeutic sessions through their AI solution, which was a key area APN was hoping to improve.
Andrea Boorse, senior manager of operations at APN, shared that their clients have two individual therapy sessions each week, rather than one—which means double the documentation. When they became aware of AI solutions that could listen in on sessions and help with that documentation, they decided to test the waters with Eleos’s Scribe solution, which automatically transforms raw conversations into progress note suggestions.
APN found that the tool started to understand and recognize therapists’ style and language, making the notes get more specific and tailored to each client. This has been a big help for APN since it now takes an average of 11 minutes to complete a note, compared to the industry standard of 15 minutes.
Embarking on AI implementation
With benefits like APN has experienced, I’ve seen a shared, cautious excitement across our industry that continues to suffer from provider and staffing burnout and attrition. By removing some of these administrative burdens, they hope to combat staffing issues and improve patient reach and care.
And while there is good reason to remain cautious, I think providers can confidently move towards AI solutions by focusing on three key areas.
Generative AI, until recently an uncharted frontier, is now encountering regulatory roadblocks. Fueled by minimal oversight, its meteoric rise is slowing as frameworks take shape. Businesses and users alike brace for the ripple effect, wondering how increased scrutiny will reshape this booming sector.
While AI automation could revolutionize efficiency and speed up processes in many customer-facing industries, healthcare demands a different approach. Here, “consumers” are patients, and their data is deeply personal: their health information. In this highly sensitive and regulated field, caution takes center stage.
The healthcare industry’s embrace of AI is inevitable, but the optimal areas for its impact are still being mapped out. As new regulations aim to curb this disruptive technology, a crucial balance must be struck: fostering smarter, more efficient AI tools while ensuring compliance and trust.
The Need for Regulation
Regulatory mechanisms and compliance procedures will play a crucial role in minimizing risk and optimizing AI applicability in the coming decade.
These regulations must be developed to effectively safeguard sensitive patient data and prevent unauthorized access, breaches, and misuse—necessary steps in gaining patient trust in these tools. Imagine the added friction of AI systems that misdiagnose patients, spew incorrect information, or suffer from regular data leaks. The legal and financial implications would be dire.
Optimized workflows simply cannot come at the cost of unaddressed risks. Regulated and responsible AI is the only way forward. And in order to achieve both, three foundational pillars must be met: explainability, control, and compliance.
Imagine this: physicians spend more time with their patients than with their paperwork. Billing is quick and accurate, with minimal denials. Healthcare workers enjoy a positive work/life balance. Thanks to the rapid advancement of AI, this vision of healthcare is becoming increasingly possible.
Health system leaders are already investing toward this ideal state. From roundtable discussions at Healthcare Financial Management Association and Becker’s Healthcare to Zoom chats every week, I’ve connected with many C-suite executives at health systems about their expectations for AI. There is, across the board, a clear set of priorities for the next one to two years. The overarching vision is not just to integrate new technologies but to do so in a way that delivers tangible improvements in workforce experiences and satisfaction, revenues and costs, and patient care outcomes.
Here are a few resounding themes that I’ve heard.
Proving ROI
Proving ROI on AI investments is crucial: put plainly, you want to ensure you’re getting more than enough bang for your buck. Applications of AI need to map back clearly to measurable cost and revenue impacts. Health system CFOs expect predictable ROI and are screening new technologies closely.
Many AI tools on the administrative side can meet this proof of hard ROI. For example, organizations like ApolloMD have experienced significant improvements in coding efficiency and revenue capture by minimizing coding errors and denials through autonomous coding.
While vendors typically report impressive ROI from their technology, any vendor worth its salt will agree to a proof of concept allowing you to test and validate impact for your organization. For example, an easy way to build confidence in autonomous coding is to compare coding results between your team and the AI system before committing to go-live.
Increasing end-to-end strategies
Many AI tools have surfaced to address a single use case. However, health system leaders are more interested in comprehensive, integrated solutions across departments. Consider the case of ambient documentation and autonomous coding: ambient documentation works as a medical scribe using AI to document clinician-patient encounters, and then autonomous coding steps in as a medical coder to translate and assign the necessary codes for billing.
These types of end-to-end strategies are more compelling and impactful. Health system CEOs increasingly gravitate toward them to ease administrative burdens, speed up visit-related processes, and enhance patient outcomes. The market is supporting this expectation: Abridge, an ambient documentation platform, recently raised $150 million in funding, and Google Cloud added an autonomous medical coding solution to its marketplace earlier this year. Used in conjunction, these technologies offer more integrated – and more valuable – strategies for health systems.
Moving from inventing the first wheel ever to discovering the use of Artificial intelligence, we have come a long way. The world is changing for the better, and technological advancement has impacted numerous industries. And the healthcare industry is no exception.
The pandemic has highlighted several gaps in the accessibility of services to patients. Healthcare facilities have had to question old operating methods and adapt to better solutions for providing patients with better care. Moving into 2022, we can observe certain advancements in this sector. These are predictive of what improvements are likely to occur in the future. Listed below are some health-tech trends that are likely to impact the quality of care patients receive profoundly.
Better predictive analytics
The role of data is becoming prominent in improving healthcare services. Data helps identify trends in population health, thereby also helping to identify people at higher risk of developing specific medical issues. Such analysis includes gathering data from hospitals, specialists, primary care providers, and pharmacies. The information will help close gaps in providing patients with proper treatment on time. It will also help healthcare facilities manage a shortfall of resources during emergencies such as a pandemic.
Predictive analytics are likely to become more accurate and efficient in the future with more innovative data collection tools. It will help improve healthcare systems engineering, leading to better management and delivery of high-quality patient care.
Telehealth will become more common
In the past, access to healthcare depended on whether a patient could make it to a hospital or not. However, as communication and collaboration between different geographical locations increases, healthcare services will also expand. Telehealth is not a new idea, but it will gain popularity in the coming years. Doctors and nurse practitioners will be able to counsel patients over apps such as Zoom and other dedicated health portals.
Moreover, at-home testing kits will become more accessible, enabling patients to maintain privacy. According to the American Hospital Association, most healthcare services will be delivered at home or virtually by 2040. It will make healthcare much more accessible to people, especially those who live in remote areas.
While the COVID-19 pandemic forced healthcare into a reactionary crisis state in 2020, 2021 offered an opportunity to rethink traditional care delivery models. Divergent views on vaccines, powerful COVID-19 variants and ongoing capacity issues have shown that providers, and the technology companies that support them, will need to continue to evolve in order to serve patients effectively.
As we look towards 2022, experts at Wolters Kluwer Health, a clinical technology and evidence-based solutions provider, outlined their predictions for next year and what they think it will take to properly equip providers to deliver the best care everywhere.
Building trust in an age of digital information overload
Digital health investment in 2021 has focused mostly on technology innovation and workflow improvements. What I’m seeing in the digital health space is akin to the implementation of EMRs, which really focused on the technology itself and not the content inside, which creates the experience for both clinical users and consumers. What’s missing from digital health strategy, and what providers will need to focus on in 2022, is increasing access to high-quality, evidence-based health content that consumers and providers alike can trust and understand. This ease of access is crucially important to overcome the infodemic of COVID-19, with an influx of misleading and rapidly evolving information we’ve seen expand across all areas of healthcare. Effective, engaging digital health requires more than the right technology, but a full-fledged experience that informs and motivates consumers towards evidence-based action.
Jason Burum, general manager, Healthcare Provider Segment, Clinical Effectiveness, at Wolters Kluwer, Health
More compliance, less burden
The pressures of COVID-19 spurred USP to issue interim guidance that provided flexibility for compounding pharmacies, but 2022 is likely to represent a return to stricter compliance. In September, USP issued a Notice of Intent to Revise (NITR) for both USP <797> and USP <795>. With COVID-19 cases continuing to surge across the country, I anticipate hospitals and pharmacy staff in 2022 will increasingly rely on expert solutions and technology to automate and standardize compounding operations in accordance with best practices and the latest compliance requirements. Burnout and technician shortages are happening in pharmacies too and software tools will help alleviate burdens pharmacy staff face right now.
Annie Lambert, PharmD, BCSCP, Clinical Program Manager for Compliance Solutions for Clinical Surveillance & Compliance, Wolters Kluwer, Health
Pitting AI against HAIs
Data show that while hospitals have allocated more resources to infection prevention and control efforts to contain the spread of COVID-19, it has largely come at the expense of controlling other far too common healthcare-associated infections (HAIs). It’s true that a larger volume of sicker patients at higher risk of infection and sepsis have been admitted to the hospital over the last year, but the CDC concluded that 2020 increases in HAIs were also a result of lacking surge capacity and other operational challenges. Looking ahead to 2022, as hospitals take aim at controlling all HAIs in addition to COVID-19 with more resilient care teams, they will be looking more closely than ever at AI-powered technology to support proactive and real-time monitoring of patients to empower staff with quick risk identification abilities and opportunities for earlier clinical intervention.
Mackenzie Weise, MPH, CIC, Infection Prevention Clinical Program Manager for Clinical Surveillance & Compliance, Wolters Kluwer, Health
Telemedicine grows up
Contrary to some news stories, telemedicine will prove resilient well past the pandemic and will establish itself as a permanent, significant fixture in the healthcare ecosystem. In 2022, I expect healthcare providers themselves will strengthen and formalize training to research and promote telehealth best practices to their clinicians. It’s already happening, and I expect to see specialties like mental health and urgent care shifting to a predominantly virtual model in 2022. Ultimately, I believe that the rise of telehealth will drive more dialogue around modes of access as an issue not only of tech but also equity in the years to come. This in turn will have big impacts in the future of medical practice.
Vikram Savkar, vice president and general manager, Medicine Segment of Health Learning, Research & Practice
Response by Richard Boyd, CEO and Co-founder, Tanjo.
Richard Boyd
One question healthcare technology professionals should be asking right now: How can I use AI and machine learning to create better communicate with patients to create better health outcomes?
Whether it’s a biological virus or a mind virus — the thing that makes it a pandemic or an insurrection crisis situation — is human behavior. The rapidly evolving nature of a crisis, and complex black swan events that are likely to come in the future, requires more than judgment calls by leaders with incomplete information.
We can absolutely train a synthetic model of a human being to predict how that person would behave in a given situation.
With the dramatic rise of telehealth as a result of COVID-19, AI is being used to create a data-driven patient-centered platform that benefits patients and caregivers in their quest for better health. 3DBioMe is a machine-learning and AI data analysis system that brings this interactive 3-D model of the major physiological systems of the body to the fight against COVID and other health issues.
Using AI a healthcare architect can quickly and dramatically explain the environmental and social choice determinants of all health interventions and outcomes showing the effect of those choices over time to a patient in a meaningful and persuasive way.
By John Danaher, MD, president, global clinical solutions, Elsevier.
John Danaher, MD
At the beginning of last year, we all had our own thoughts on how the year would unfold. However, a few months into 2020, we realized that the year would be quite different than we previously imagined because of the COVID-19 pandemic. With 2021 underway, we will continue to witness the digital transformation of the healthcare industry that was accelerated by the COVID-19 pandemic.
Clinicians were quick to embrace different types of innovative technology, such as telemedicine platforms and non-contact solutions to track patient vitals, that allowed them to provide patient care remotely. I believe that in 2021, we will continue to see an evolution of technology to assist clinicians and widespread adoption of digital health services. I also expect the industry will take key learnings with them as we move towards the future, such as the importance of building more trust in science and data.
Investments in AI are paying off
We have seen the impact of AI in the fight against COVID-19, specifically in the diagnosis and tracking of cases, predicting future outbreaks and assisting in selecting treatment plans.
I hope to see more infections decline as populations receive access to the COVID-19 vaccines and I see a renewed focus in how AI can help healthcare systems recover from the pandemic. Artificial Intelligence will be paramount in aiding many healthcare systems’ return to their regular operations as they were pre-pandemic. Artificial intelligence helps systems work faster to address the backlog of patient cases across other diseases and conditions that were postponed due to the pandemic, and deal with the financial strains caused by the virus. These tools can be used in revenue cycle management to assist with staffing, bed and device management, and provide a better understanding of patient utilization.
Artificial intelligence will continue to play a larger role as telemedicine tools and solutions rise in popularity.
Widespread use of telemedicine
One of the longest lasting effects of this pandemic is how clinicians have adjusted their delivery of care. The use of telemedicine applications is now a widely used practice, with the U.S. seeing an increase of 154% in telehealth visits in March 2020, compared to the same time period in 2019. There’s no doubt that the rise in the usage of telehealth services have benefited both healthcare providers and patients.
Mainly, the adoption of services has decreased the number of patients in medical offices seeking non-emergency care and ultimately minimizing the risk of exposure to COVID-19. While telemedicine will not replace in-person care, it will remain a necessity in 2021 and beyond. As patients are now more accustomed to the convenient delivery of care services, they will be more inclined to expect these remote services, along with other services, such as drive through testing sites and at-home delivery of prescription medications that do not require in-person visits.
Artificial intelligence (AI) is transforming healthcare, especially on its clinical side, where 62% of providers have already adopted an AI strategy. The American Hospital Association recently reported that when successfully implemented, clinical AI can improve patient outcomes and lower costs at each stage of the care cycle, from prevention and detection to diagnosis and treatment. Expect to see continued growth in AI adoption in 2020.
Here are a few of the most exciting AI trends to watch for:
Social Determinants of Health (SDoH) will become a core focus for healthcare AI solutions: AI has significant potential for helping reduce socioeconomic barriers to care. Across 2019, we have seen investment by CMS in the Accountable Health Communities Model, which is the first model to include social determinants of health. This model codifies what we already know — that socioeconomic factors influence an individual’s health and risk. Emerging AI technologies are actively creating value for patients by helping to make sense of large socioeconomic and environmental datasets, driving meaningful investments and action that will help to prevent avoidable utilization and guide effective distribution of community resources.
We will start to move into the “Slope of Enlightenment” within the Hype Cycle: Healthcare AI will move out of the “Trough of Disillusionment” as more evidence of AI’s ability to improve health outcomes emerge. AI-related topics will continue to gain prominence in research and the media. And the results of funded projects and pilots will become available to the broader industry.
The AI discussion will broaden beyond imaging and natural language processing: With the exponential increase in patient data, it’s only logical that it’s time for AI – the best way to synthesize that data – to have its moment. While imaging and natural language processing have dominated the healthcare AI conversation over the past few years, 2020 will mark an expanded understanding of AI solutions to include those focused on clinical decision support. These solutions integrate into existing clinical workflows to help direct resources to modifiable patients at risk of a target adverse event. Expect to see more investment and discussion around these solutions across 2020.
Moving from inventing the first wheel ever to discovering the use of Artificial intelligence, we have come a long way. The world is changing for the better, and technological advancement has impacted numerous industries. And the healthcare industry is no exception.
